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We propose an original model for inferring team strengths using a Markov Random Field, which can be used to generate historical estimates of the offensive and defensive strengths of a team over time. ...
A popular approach for large scale data annotation tasks is crowdsourcing, wherein each data point is labeled by multiple noisy annotators. We consider the problem of inferring ground truth from noisy...
Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. A...
A method is presented to estimate the probability distributions of climate system properties based on a hierarchical Bayesian model. At the base of the model, we use simulations of a climate model i...
A beam of protons is produced by a linear charged particle accelerator, then focused through the use of successive quadrupoles. The initial state of the beamis unknown, interms of particle position ...
Our concern is selecting the concentration matrix's nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional de...
In this paper, we propose a novel inference method for dynamic genetic networks which makes it possible to deal with a number of time measurements n much smaller than the number of genes p. The appr...
A key aim of systems biology is to unravel the regulatory interactions among genes and gene products in a cell. Here we investigate a graphical model that treats the observed gene expression over ti...
Markov chains are a natural and well understood tool for describing one-dimensional patterns in time or space. We show how to infer k-th order Markov chains, for arbitrary k, from finite data by app...

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